The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation
Open accessAbstract
Background: Recently, it has been proposed that epigenetic variation may contribute to the risk of complex genetic diseases like cancer. We aimed to demonstrate that epigenetic changes in normal cells, collected years in advance of the first signs of morphological transformation, can predict the risk of such transformation. Methods: We analyzed DNA methylation (DNAm) profiles of over 27,000 CpGs in cytologically normal cells of the uterine cervix from 152 women in a prospective nested case-control study. We used statistics based on differential variability to identify CpGs associated with the risk of transformation and a novel statistical algorithm called EVORA (Epigenetic Variable Outliers for Risk prediction Analysis) to make predictions. Results: We observed many CpGs that were differentially variable between women who developed a non-invasive cervical neoplasia within 3 years of sample collection and those that remained disease-free. These CpGs exhibited heterogeneous outlier methylation profiles and overlapped strongly with CpGs undergoing age-associated DNA methylation changes in normal tissue. Using EVORA, we demonstrate that the risk of cervical neoplasia can be predicted in blind test sets (AUC = 0.66 (0.58 to 0.75)), and that assessment of DNAm variability allows more reliable identification of risk-associated CpGs than statistics based on differences in mean methylation levels. In independent data, EVORA showed high sensitivity and specificity to detect pre-invasive neoplasia and cervical cancer (AUC = 0.93 (0.86 to 1) and AUC = 1, respectively). Conclusions: We demonstrate that the risk of neoplastic transformation can be predicted from DNA methylation profiles in the morphologically normal cell of origin of an epithelial cancer. Having profiled only 0.1% of CpGs in the human genome, studies of wider coverage are likely to yield improved predictive and diagnostic models with the accuracy needed for clinical application. Trial registration: The ARTISTIC trial is registered with the International Standard Randomised Controlled Trial Number ISRCTN25417821.
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Recurrent Fusion of TMPRSS2 and ETS Transcription Factor Genes in Prostate Cancer
Tomlins, S. A., Rhodes, D. R., Perner, S., Dhanasekaran, S. M., Mehra, R., Sun, X.-W., Varambally, S., Cao, X., Tchinda, J., Kuefer, R., et al. (2005) · Science · doi:10.1126/science.1117679 · Cited by 3,846
Linking DNA methylation and histone modification: patterns and paradigms
Cedar, H., & Bergman, Y. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2540 · Cited by 2,267 · cited 1×
“Over the years extensive evidence has accumulated to demonstrate that PCGT methylation is crucially involved in carcinogenesis [33-37].”
BackgroundDu, P., Zhang, X., Huang, C.-C., Jafari, N., Kibbe, W. A., Hou, L., & Lin, S. M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-587 · Cited by 2,136
Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome
Sandoval, J., Heyn, H., Moran, S., Serra-Musach, J., Pujana, M. A., Bibikova, M., & Esteller, M. (2011) · Epigenetics · doi:10.4161/epi.6.6.16196 · Cited by 990 · cited 1×
“Thus, it will be interesting to explore the spatial variability around the identified CpG risk sites with a more comprehensive and unbiased technology such as the Infinium 450 K methylation beadarray [51], as this may reveal a similar loss of methylation boundaries surrounding these sites.”
Background
Cited by
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075
Epigenetic modulators, modifiers and mediators in cancer aetiology and progression
Feinberg, A. P., Koldobskiy, M. A., & Göndör, A. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.13 · Cited by 841
Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host
Timp, W., & Feinberg, A. P. (2013) · Nature Reviews Cancer · doi:10.1038/nrc3486 · Cited by 586
Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets
Zhang, S., Xiao, X., Yi, Y., Wang, X., Zhu, L., Shen, Y., Lin, D., & Wu, C. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01848-7 · Cited by 399
Pan-Cancer Landscape of Aberrant DNA Methylation across Human Tumors
Saghafinia, S., Mina, M., Riggi, N., Hanahan, D., & Ciriello, G. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.09.082 · Cited by 357
Metadata
- Authors
- Teschendorff, A. E., Jones, A., Fiegl, H., Sargent, A., Zhuang, J. J., Kitchener, H. C., & Widschwendter, M.
- Venue
- Genome Medicine
- Published
- 2012-03-27
- Volume
- 4
- Issue
- 3
- Pages
- 24-24
- ISSN
- 1756-994X (ISSN-L)
- OpenAlex
- W2120731353
- Cited by
- 182
- References
- 7
- FWCI
- 5.00
- Type
- journal article
- Language
- en
- Open access
- gold
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- Teschendorff, A. E., Jones, A., Fiegl, H., Sargent, A., Zhuang, J. J., Kitchener, H. C., & Widschwendter, M. (2012). Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation. Genome Medicine. https://doi.org/10.1186/gm323
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